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Sungchul Kim

28 papers hereh-index 253.5k citations68 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author24
  • last author2

Across the 26 of 28 papers where every author was matched, so the position is known.

fields
  • cs.LG8
  • cs.SI8
  • cs.CL6
  • cs.IR4
  • cs.AI1
  • cs.CV1
same name
  • Sungchul Kim — 20 papers, h 10
  • Sungchul Kim — 20 papers, h 9
  • Sungchul Kim — 6 papers, h 4
  • Sungchul Kim — 1 paper, h 3
  • Sungchul Kim — 1 paper, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182023
most citedBias and Fairness in Large Language Models: A Survey

59 citations · 175 across the 19 of their papers we have counts for

collaborators
Showing cs.IRShow all

4 papers · 1 filter

cs.IR2022★ 8 cited

Implicit Session Contexts for Next-Item Recommendations

Sejoon Oh, Ankur Bhardwaj, Jongseok Han +3

Session-based recommender systems capture the short-term interest of a user within a session. Session contexts (i.e., a user's high-level interests or intents within a session) are…

cs.IR2022★ 15 cited

Bundle MCR: Towards Conversational Bundle Recommendation

Zhankui He, Handong Zhao, Tong Yu +3

Bundle recommender systems recommend sets of items (e.g., pants, shirt, and shoes) to users, but they often suffer from two issues: significant interaction sparsity and a large out…

cs.IR2021

Personalized Visualization Recommendation

Xin Qian, Ryan A. Rossi, Fan Du +5

Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset and not the actual user and their past visualization feedback. These…

cs.IR2020★ 11 cited

ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data

Xin Qian, Ryan A. Rossi, Fan Du +5

Visualization recommendation seeks to generate, score, and recommend to users useful visualizations automatically, and are fundamentally important for exploring and gaining insight…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.